Software applications can be viewed as a consumer of resources. Accordingly, it is often desirable for engineers to perform a “capacity analysis” of an application—i.e., measure and/or predict the amount or rate of consumption by that application of various resources, such as time, CPU, I/O, memory, network bandwidth, database connections, threads, database server memory, database buffers, and the like. In this way, the engineer can measure the current behavior, characteristics, and overall health of the system. Capacity analysis also helps the engineer determine whether the hardware and operating system on which the application is running will have sufficient resources in the future when resource consumption rates may change.
Currently known methods of performing capacity analysis are unsatisfactory in a number of respects. For example, it is common to exercise an application by applying a “workload” to its functional interfaces and then measuring its performance using various metrics. Such methods are time-consuming and difficult to perform since applications often do not consume resources in a predictable manner. Furthermore, such methods require an engineer to create a custom workload for many different components of the application, which may change over time, requiring the engineer to revise the workload.
Accordingly, there is a need for improved systems and methods for performing capacity analysis and otherwise analyzing the performance of software applications.